Organizations must show greater agility when it comes to looking at and acting upon the answers that data can provide. But determining which is the right data to use is paramount to success. And knowing how to interpret and analyze that data is the challenge facing us all. Increasingly, this is bringing marketing and data science teams closer together.
In a recent webinar, hosted by Argyle Executive Forum and moderated by our client Alex Allen, CMO at Spring Venture Group, I was part of a fascinating conversation about how enterprise-level businesses are using their data – both first party and third party – to influence the decisions they are making about the customer journey.
We were joined on the panel by senior marketing executives from PepsiCo, TIAA and Avis Budget Group, and the first thing that struck me was not just the different types of data being used, but the different ways they were using it, and the different results they achieved.
Below are a few things I learnt during the session, giving me some further considerations that I plan to use when looking at our own customers' data.
Digital data, offline conversion
Spring Venture Group has used its data to connect what is happening on digital channels with the offline experience. To quote Alex Allen from the webinar, “we wanted to see whether there were actions during the online sessions that were indicative of higher levels of intent in order to prioritize who gets a phone call. To do that, we analyzed our customers’ digital body language, or their behavior on the page.”
What Alex and his team found was, by looking at those digital signals from the customer, there was a range of predictive signals that could inform the prioritization process they employ when making an outbound phone call. The most revealing of which was that if a customer was typing very fast while filling in a form, they are 15-20% more likely to purchase an insurance policy on the phone. Indeed, fast typing speed is indicative of high confidence and excitement – a digital manifestation of the consumer’s cognitive and emotional state.
Before analyzing the data in this depth, Alex had a theory that those customers who were more tech-savvy or more comfortable using mobile forms were the ones more likely to convert offline. The data that he has gathered has gone some way to confirming that hypothesis, and the joy of data analytics and data science is that there is always more to learn or interpret.
Attitudes and behaviors are more relevant than demographics
When organizations, large and small, are looking at their data, they more often than not segment their audiences by traditional demographics. But to get the next level of insight from our data, we need to start using advanced segmentation – looking at how customers are behaving online and interpreting their emotions and attitudes from that data.
And especially in a multi-channel environment, with so many disparate touchpoints, it is becoming increasingly difficult to map traditional demographic segmentation by the sheer quantity of data these devices produce.
For Neal Zamore, Senior VP, Global Digital Customer Experience at Avis Budget Group, this is particularly poignant. Remembering something he said during our discussion, “we have found that our customers’ online behavior is more natural than when they’re at the rental desk. Which means that we can see what they naturally buy in clusters – adding in a child seat, a sat-nav system etc. This data better informs us of which packages to promote to customers, and how to upsell both online and offline.” By being able to use the data to create informative clusters, they are able to create a more satisfying journey for their customers.
Experiment, experiment, experiment
When it comes to looking for new insight from our data, we need to take more risks. This can be more difficult in larger organizations, but it doesn’t have to be. Even the largest enterprises, like Amazon, are constantly experimenting in order to deliver transformative experiences.
Esperanza Teasdale, Senior Director, Shopper Marketing at PepsiCo raised a particularly valid point: “It sounds like a lot of work to be experimenting all the time, but it’s not. All we need is a few small wins in test and learn environment, and the idea of proof of concept. Once we can show true impact, we can justify a much more scaled solution to the point where we can mine data in real time and take action against it.”
We’re only scratching the surface
The most exciting thing about data science and data analysis is that the data that we are looking at will never be complete. We are only scratching the surface of what we can achieve and the lifetime value we can gain from our customers. To quote Neal Zamore again, “we’re drowning in data, but thirsty for information.”
And I’ve only just scratched the surface of what was discussed in the webinar too. A full recording is now available to watch in full on demand, and there was so much fantastic insight that I couldn’t squeeze into this blog, including:
- How brands decide which data is relevant and important
- How we operationalize the learnings from our data analysis
- How we take the step from our data being descriptive to prescriptive